Prediction of kinase-specific phosphorylation sites using conditional random fields.
BACKGROUND
Phosphorylation is a crucial post-translational protein modification mechanism with important regulatory functions in biological systems. It is catalyzed by a group of enzymes called kinases, each of which recognizes certain target sites in its substrate proteins. Several authors have built computational models trained from sets of experimentally validated phosphorylation sites to predict these target sites for each given kinase. All of these models suffer from certain limitations, such as the fact that they do not take into account the dependencies between amino acid motifs within protein sequences in a global fashion.


RESULTS
We propose a novel approach to predict phosphorylation sites from the protein sequence. The method uses a positive dataset to train a conditional random field (CRF) model. The negative training dataset is used to specify the decision threshold corresponding to a desired false positive rate. Application of the method on experimentally verified benchmark phosphorylation data (Phospho.ELM) shows that it performs well compared to existing methods for most kinases. This is to our knowledge that the first report of the use of CRFs to predict post-translational modification sites in protein sequences.


BACKGROUND
The source code of the implementation, called CRPhos, is available from http://www.ptools.ua.ac.be/CRPhos/
Dang
Thanh Hai
TH
Intelligent Systems Laboratory and Advanced Database Research and Modelling, Department of Mathematics and Computer Science, Middelheimlaan 1, B-2020 Antwerpen, Belgium.
Van Leemput
Koenraad
K
Verschoren
Alain
A
Laukens
Kris
K
D000465:Algorithms; D019295:Computational Biology / Q000379:methods; D030562:Databases, Protein; D010766:Phosphorylation; D011494:Protein Kinases / Q000737:chemistry / Q000378:metabolism*; D020539:Sequence Analysis, Protein
D011494:Protein Kinases